The explosion in the number of online relational databases has created a growing interest in leveraging relevant, high-quality information present in these relational databases. However, a number of factors affect the degree of benefit end-users can derive from such databases, including, e.g., how well the database has been designed, retrieved, and presented.
Relational database design has evolved from what was once an “artistic” endeavor to a scientific one. This evolution in relational database design has been made possible with certain vendor-supplied tools that aid a developer in designing the most efficient databases possible. Many of these tools offer interactive modeling capabilities using a simplified data modeling approach. However, while these tools may help create an optimized, efficient database, end-users often have difficulty understanding the meaning inherent in a database's conceptual model, leading to a lack in ontological clarity.
One standard and efficient method of retrieving information from relational databases may be referred to/described as systematic retrieval. That is, the needs of an end-user, e.g., data being sought by the end-user, are described in a formal query, and a database management system retrieves the desired data. There are several situations, however, where systematic retrieval fails to provide an optimal experience for the end-user. For example, an end-user may not be familiar with the principles employed by the database management system to organize data (the data model) or the definition (schema) of the particular database to be accessed. Oftentimes, an end-user's retrieval target is vague, e.g., the end-user is merely looking for something interesting or suitable for some purpose, rather than a specific target or piece of data. Alternatively, the end-user may simply fail to provide sufficient information that will result in a fruitful query. To wit, studies have found that the average number of keyword terms per query that end-users provide in a Google search is merely about 2.286 terms.
In addition to tools and algorithms that assist in the development of databases and the subsequent retrieval of information from the databases, a variety of systems also exist that have begun to bring together the advantages of information visualization and current information retrieval technology. Visual representations of data may significantly enhance a user's ability to understand these complicated relations and underlying structures in such relational databases. Some visualization interfaces for information retrieval systems visualize ranked, query-document similarity and clustering. Research regarding such visualization interfaes generally falls into the categories of data visualization and semantic information presentation. However, while most research focuses on visualizing data from an analytical point of view, end-users often rely on database searches for better recall and engagement.
In the area of data visualization, some research is being/has been conducted regarding the automatic rendering of entity-relationship diagrams. An example of such research is a text entitled “Constraints in Graph Drawing Algorithms,” by Roberto Tamassia, Constraints Volume 3, Number 1 (April 1998). Other research is directed to the presention of a graphical browser or interface for relational databases, such as the article entitled “InGRAPH: graphical interface for a fully object-oriented database system,” by Xuequn Wu and Guido Dinkhoff, In Proceedings of the 1993 ACM/SIGAPP symposium on Applied computing: states of the art and practice (SAC '93. The text “An Algorithm for Drawing General Undirected Graphs,” by Tomihisa Kamada and Satoru Kawai, Information Processing Letters, 31:7-15, 1988 presents a visualization framework for translating abstract objects and relations, typically in textual forms, into pictorial representations. In this framework, abstract objects and relations are mapped to graphical objects by user-defined mapping constraints. However, such research has been focused on merely the analytical aspects of making these relationships clearer, rather than focusing on end-user consumption.